Abstract: A machine learning assessment system is provided. The system identifies multiple datasets and multiple machine learning (ML) modeling algorithms based on the client profile. The system assesses a cost of data collection for each dataset of the multiple datasets. The system assesses a performance metric for each ML modeling algorithm of the multiple modeling algorithms. The system recommends a dataset from the multiple datasets and an ML modeling algorithm from the multiple ML modeling algorithm based on the assessed costs of data collection for the multiple datasets and the assessed performance metrics for the multiple ML modeling algorithms.

Abstract: A system, computer program product, and method to deriving a cost model and dynamic adjustment of the derived model responsive to dynamic modification of one or more of the resources in a hybrid shared resource environment. Resources and corresponding configuration information are collected while monitoring runtime utilization of resource performance. As changes to the resources are discovered, the changes are subject to an assessment. A hybrid cost model is derived and configured to account for the one or more resources. The derived hybrid cost model is leveraged to conduct a multi-dimensional resource evaluation of the assessed changed configuration information. Responsive to the multi-dimensional evaluation, a generated resource utilization optimization of the one or more resources is selectively implemented.

Abstract: A system, computer program product, and method to deriving a cost model and dynamic adjustment of the derived model responsive to dynamic modification of one or more of the resources in a hybrid shared resource environment. Resources and corresponding configuration information are collected while monitoring runtime utilization of resource performance. As changes to the resources are discovered, the changes are subject to an assessment. A hybrid cost model is derived and configured to account for the one or more resources. The derived hybrid cost model is leveraged to conduct a multi-dimensional resource evaluation of the assessed changed configuration information. Responsive to the multi-dimensional evaluation, a generated resource utilization optimization of the one or more resources is selectively implemented.

Abstract: A machine learning assessment system is provided. The system identifies multiple datasets and multiple machine learning (ML) modeling algorithms based on the client profile. The system assesses a cost of data collection for each dataset of the multiple datasets. The system assesses a performance metric for each ML modeling algorithm of the multiple modeling algorithms. The system recommends a dataset from the multiple datasets and an ML modeling algorithm from the multiple ML modeling algorithm based on the assessed costs of data collection for the multiple datasets and the assessed performance metrics for the multiple ML modeling algorithms.

Abstract: A system, computer program product, and method are provided for dynamically orchestrating a prerequisite driven codified infrastructure. A prerequisite defines deployment of one or more resources in the codified infrastructure through the use of a codified logic expression. One or more probes are created and leveraged to monitor a dynamic resource state for one or more prerequisites. A new codified infrastructure is generated based on the change(s) to the monitored dynamic resource state for the one or more prerequisites. The identified one or more changes are applied to the new codified infrastructure and the new codified infrastructure is executed based on the applied change(s).

Abstract: A system, computer program product, and method are provided for dynamically orchestrating a prerequisite driven codified infrastructure. A prerequisite defines deployment of one or more resources in the codified infrastructure through the use of a codified logic expression. One or more probes are created and leveraged to monitor a dynamic resource state for one or more prerequisites. A new codified infrastructure is generated based on the change(s) to the monitored dynamic resource state for the one or more prerequisites. The identified one or more changes are applied to the new codified infrastructure and the new codified infrastructure is executed based on the applied change(s).

Abstract: A system, computer program product, and method are provided to automate management of a computer architecture as represented in a codified infrastructure with one or more embedded dynamically labeled resources. The codified infrastructure is subject to interpretation within the context of the dynamically labeled resources. The dynamic labels are converted to static labels, after which the corresponding architecture is deployed. The deployment architecture continues to be updated when the meaning of the dynamic labels change.

Abstract: A system, computer program product, and method are provided to automate management of a computer architecture as represented in a codified infrastructure with one or more embedded dynamically labeled resources. The codified infrastructure is subject to interpretation within the context of the dynamically labeled resources. The dynamic labels are converted to static labels, after which the corresponding architecture is deployed. The deployment architecture continues to be updated when the meaning of the dynamic labels change.

Abstract: An aspect includes the automatic generation of blueprints for orchestration engines from discovered workload representations. A computer-implemented method includes receiving, using a processor, a description of a workload executing in a source computing environment. The description of the workload is mapped, using the processor, to one or more resource descriptors located in a repository of resource descriptors describing resources available in a target computing environment. A declarative representation of the workload in the target computing environment is generated, using the processor, based at least in part on the mapping and the description of the workload. The declarative representation includes a specification of resources required by the workload in the target environment.

Abstract: An aspect includes the automatic generation of blueprints for orchestration engines from discovered workload representations. A computer-implemented method includes receiving, using a processor, a description of a workload executing in a source computing environment. The description of the workload is mapped, using the processor, to one or more resource descriptors located in a repository of resource descriptors describing resources available in a target computing environment. A declarative representation of the workload in the target computing environment is generated, using the processor, based at least in part on the mapping and the description of the workload. The declarative representation includes a specification of resources required by the workload in the target environment.